Many human diseases are inherently quantitative (e.g., hypertension). Others are generally viewed as dichotomous (e.g., diabetes) but arc closely associated with intermediate quantitative phenotypes (e.g., glucose tolerance). Quantitative traits are influenced by multiple genetic loci (called quantitative trait loci, QTLs) as well as the environmental. Our long-term goal is to develop improved statistical methods for mapping multiple QTLs in experimental crosses. We focus on mouse and rat models of human disease. The central statistical problem in QTL mapping is one of model selection: one seeks to identify an appropriate QTL model, including the number and locations of QTLs and the identity of QTL:QTL interactions (called epistasis). The simultaneous mapping of multiple QTLs (versus methods, such as interval mapping, which model a single QTL at a time) has the advantage of better separating linked QTLs and allows the identification of interactions between QTLs. We further seek to develop and distribute computer software implementing such methods, in order to make the best QTL mapping methods widely available to geneticists. Toward these goals, the current proposal has the following specific aims: (1) Develop practical model selection procedures for mapping multiple QTLs in the presence of epistatic interactions and missing genotype data. (2) Develop improved methods for the analysis of recombinant inbred (RI) lines, including RIX lines and RI lines developed from multiple parental strains. (3) Develop and distribute the comprehensive QTL mapping software, R/qtl. Software development is a particularly important aspect of this work, as QTL mapping methods, no matter how refined,, will not be used if they are not implemented in user-friendly computer software. Further, the proper assessment off the performance of the methods developed towards aims (1) and (2), via large-scale computer simulations and the analysis of experimental data, requires their implementation in efficient computer software.